Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3677
Missing cells5665
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory589.2 B

Variable types

Text3
Categorical10
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 3 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%)Imbalance
super_built_up_area has 1802 (49.0%) missing valuesMissing
built_up_area has 1987 (54.0%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73095233)Skewed
built_up_area is highly skewed (γ1 = 40.70657243)Skewed
carpet_area is highly skewed (γ1 = 24.33323909)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 462 (12.6%) zerosZeros

Reproduction

Analysis started2025-09-27 10:15:30.143832
Analysis finished2025-09-27 10:16:02.133301
Duration31.99 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

sector
Text

Distinct114
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
2025-09-27T15:46:02.907310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3138428
Min length3

Characters and Unicode

Total characters34247
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 95
2nd rowsector 78
3rd rowmanesar
4th rowsector 108
5th rowsector 108
ValueCountFrequency (%)
sector3448
46.7%
road178
 
2.4%
sohna166
 
2.2%
85108
 
1.5%
102107
 
1.5%
92101
 
1.4%
6992
 
1.2%
6587
 
1.2%
8187
 
1.2%
9087
 
1.2%
Other values (107)2917
39.5%
2025-09-27T15:46:03.989035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o3803
11.1%
3701
10.8%
s3693
10.8%
r3693
10.8%
e3542
10.3%
c3499
10.2%
t3459
10.1%
11075
 
3.1%
0801
 
2.3%
8778
 
2.3%
Other values (21)6203
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23286
68.0%
Decimal Number7260
 
21.2%
Space Separator3701
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3803
16.3%
s3693
15.9%
r3693
15.9%
e3542
15.2%
c3499
15.0%
t3459
14.9%
a698
 
3.0%
d249
 
1.1%
n225
 
1.0%
h203
 
0.9%
Other values (10)222
 
1.0%
Decimal Number
ValueCountFrequency (%)
11075
14.8%
0801
11.0%
8778
10.7%
9762
10.5%
6741
10.2%
7683
9.4%
2677
9.3%
3666
9.2%
5593
8.2%
4484
6.7%
Space Separator
ValueCountFrequency (%)
3701
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23286
68.0%
Common10961
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3803
16.3%
s3693
15.9%
r3693
15.9%
e3542
15.2%
c3499
15.0%
t3459
14.9%
a698
 
3.0%
d249
 
1.1%
n225
 
1.0%
h203
 
0.9%
Other values (10)222
 
1.0%
Common
ValueCountFrequency (%)
3701
33.8%
11075
 
9.8%
0801
 
7.3%
8778
 
7.1%
9762
 
7.0%
6741
 
6.8%
7683
 
6.2%
2677
 
6.2%
3666
 
6.1%
5593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII34247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o3803
11.1%
3701
10.8%
s3693
10.8%
r3693
10.8%
e3542
10.3%
c3499
10.2%
t3459
10.1%
11075
 
3.1%
0801
 
2.3%
8778
 
2.3%
Other values (21)6203
18.1%

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size219.9 KiB
flat
2818 
house
859 

Length

Max length5
Median length4
Mean length4.2336144
Min length4

Characters and Unicode

Total characters15567
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat2818
76.6%
house859
 
23.4%

Length

2025-09-27T15:46:04.400074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:04.624508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat2818
76.6%
house859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15567
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin15567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII15567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f2818
18.1%
l2818
18.1%
a2818
18.1%
t2818
18.1%
h859
 
5.5%
o859
 
5.5%
u859
 
5.5%
s859
 
5.5%
e859
 
5.5%

society
Text

Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size265.2 KiB
2025-09-27T15:46:05.852918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.869695
Min length1

Characters and Unicode

Total characters62013
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowthe roselia 2
2nd rowumang monsoon breeze
3rd rowhsiidc sidco aravali
4th rowsobha city
5th rowsobha city
ValueCountFrequency (%)
independent491
 
5.1%
the350
 
3.6%
dlf220
 
2.3%
park209
 
2.2%
city166
 
1.7%
emaar155
 
1.6%
global153
 
1.6%
m3m152
 
1.6%
signature150
 
1.6%
heights134
 
1.4%
Other values (783)7497
77.5%
2025-09-27T15:46:07.332030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e6710
 
10.8%
6003
 
9.7%
a5861
 
9.5%
r4171
 
6.7%
n4163
 
6.7%
i3830
 
6.2%
t3719
 
6.0%
s3472
 
5.6%
l2943
 
4.7%
o2755
 
4.4%
Other values (31)18386
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter55465
89.4%
Space Separator6003
 
9.7%
Decimal Number527
 
0.8%
Other Punctuation10
 
< 0.1%
Dash Punctuation8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6710
12.1%
a5861
 
10.6%
r4171
 
7.5%
n4163
 
7.5%
i3830
 
6.9%
t3719
 
6.7%
s3472
 
6.3%
l2943
 
5.3%
o2755
 
5.0%
d2488
 
4.5%
Other values (16)15353
27.7%
Decimal Number
ValueCountFrequency (%)
3207
39.3%
282
 
15.6%
175
 
14.2%
656
 
10.6%
832
 
6.1%
419
 
3.6%
517
 
3.2%
013
 
2.5%
913
 
2.5%
713
 
2.5%
Other Punctuation
ValueCountFrequency (%)
,7
70.0%
/2
 
20.0%
.1
 
10.0%
Space Separator
ValueCountFrequency (%)
6003
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin55465
89.4%
Common6548
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6710
12.1%
a5861
 
10.6%
r4171
 
7.5%
n4163
 
7.5%
i3830
 
6.9%
t3719
 
6.7%
s3472
 
6.3%
l2943
 
5.3%
o2755
 
5.0%
d2488
 
4.5%
Other values (16)15353
27.7%
Common
ValueCountFrequency (%)
6003
91.7%
3207
 
3.2%
282
 
1.3%
175
 
1.1%
656
 
0.9%
832
 
0.5%
419
 
0.3%
517
 
0.3%
013
 
0.2%
913
 
0.2%
Other values (5)31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII62013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6710
 
10.8%
6003
 
9.7%
a5861
 
9.5%
r4171
 
6.7%
n4163
 
6.7%
i3830
 
6.2%
t3719
 
6.0%
s3472
 
5.6%
l2943
 
4.7%
o2755
 
4.4%
Other values (31)18386
29.6%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:07.670106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2025-09-27T15:46:08.044158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2580
 
2.2%
1.264
 
1.7%
1.564
 
1.7%
0.963
 
1.7%
1.162
 
1.7%
1.460
 
1.6%
1.357
 
1.6%
252
 
1.4%
0.9552
 
1.4%
1.648
 
1.3%
Other values (463)3058
83.2%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
0.191
 
< 0.1%
0.28
0.2%
0.216
0.2%
0.228
0.2%
0.231
 
< 0.1%
0.246
0.2%
0.2511
0.3%
ValueCountFrequency (%)
31.51
 
< 0.1%
27.51
 
< 0.1%
262
0.1%
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
203
0.1%
19.52
0.1%
193
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:08.395486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2025-09-27T15:46:08.794109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000027
 
0.7%
800019
 
0.5%
500017
 
0.5%
1250014
 
0.4%
2222213
 
0.4%
1111113
 
0.4%
666613
 
0.4%
750012
 
0.3%
833312
 
0.3%
3333311
 
0.3%
Other values (2641)3509
95.4%
(Missing)17
 
0.5%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
531
< 0.1%
571
< 0.1%
582
0.1%
601
< 0.1%
611
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
6000001
< 0.1%
4000001
< 0.1%
3157891
< 0.1%
3083331
< 0.1%
2909481
< 0.1%
2833331
< 0.1%
2666661
< 0.1%
2611941
< 0.1%
2453981
< 0.1%
2416661
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1648
Distinct (%)45.0%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.336
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:09.173362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11232.27
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1067.73

Descriptive statistics

Standard deviation23167.507
Coefficient of variation (CV)8.0210569
Kurtosis942.02884
Mean2888.336
Median Absolute Deviation (MAD)533
Skewness29.730952
Sum10571310
Variance5.3673338 × 108
MonotonicityNot monotonic
2025-09-27T15:46:09.561973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165052
 
1.4%
195042
 
1.1%
135034
 
0.9%
200033
 
0.9%
240022
 
0.6%
180022
 
0.6%
215021
 
0.6%
190020
 
0.5%
130020
 
0.5%
157819
 
0.5%
Other values (1638)3375
91.8%
ValueCountFrequency (%)
504
0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
602
0.1%
611
 
< 0.1%
672
0.1%
701
 
< 0.1%
721
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
8750001
< 0.1%
6428571
< 0.1%
6200001
< 0.1%
5666671
< 0.1%
215517.241
< 0.1%
98977.951
< 0.1%
82781.461
< 0.1%
655172
0.1%
65261.041
< 0.1%
582281
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size399.4 KiB
2025-09-27T15:46:11.248400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.236062
Min length12

Characters and Unicode

Total characters199426
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowBuilt Up area: 695 (64.57 sq.m.)Carpet area: 595 sq.ft. (55.28 sq.m.)
2nd rowSuper Built up area 2250(209.03 sq.m.)Carpet area: 1848 sq.ft. (171.68 sq.m.)
3rd rowSuper Built up area 2588(240.43 sq.m.)Built Up area: 1900 sq.ft. (176.52 sq.m.)
4th rowSuper Built up area 2072.9(192.58 sq.m.)
5th rowSuper Built up area 1381(128.3 sq.m.)
ValueCountFrequency (%)
area5573
18.5%
sq.m3655
12.1%
up3020
 
10.0%
built2316
 
7.7%
super1875
 
6.2%
sq.ft1751
 
5.8%
sq.m.)carpet1185
 
3.9%
sq.m.)built702
 
2.3%
carpet683
 
2.3%
plot681
 
2.3%
Other values (2846)8700
28.9%
2025-09-27T15:46:12.944610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26464
 
13.3%
.20389
 
10.2%
a13154
 
6.6%
r9456
 
4.7%
e9320
 
4.7%
19205
 
4.6%
s7567
 
3.8%
q7431
 
3.7%
t7324
 
3.7%
u6770
 
3.4%
Other values (25)82346
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter82758
41.5%
Decimal Number47135
23.6%
Space Separator26464
 
13.3%
Other Punctuation23406
 
11.7%
Uppercase Letter8593
 
4.3%
Close Punctuation5535
 
2.8%
Open Punctuation5535
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a13154
15.9%
r9456
11.4%
e9320
11.3%
s7567
9.1%
q7431
9.0%
t7324
8.8%
u6770
8.2%
p6767
8.2%
m5544
6.7%
l3701
 
4.5%
Other values (5)5724
6.9%
Decimal Number
ValueCountFrequency (%)
19205
19.5%
06628
14.1%
25688
12.1%
54714
10.0%
33960
8.4%
43711
7.9%
63674
 
7.8%
73254
 
6.9%
83157
 
6.7%
93144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B3020
35.1%
S1875
21.8%
C1872
21.8%
U1145
 
13.3%
P681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
.20389
87.1%
:3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26464
100.0%
Close Punctuation
ValueCountFrequency (%)
)5535
100.0%
Open Punctuation
ValueCountFrequency (%)
(5535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common108075
54.2%
Latin91351
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a13154
14.4%
r9456
10.4%
e9320
10.2%
s7567
8.3%
q7431
8.1%
t7324
8.0%
u6770
7.4%
p6767
7.4%
m5544
 
6.1%
l3701
 
4.1%
Other values (10)14317
15.7%
Common
ValueCountFrequency (%)
26464
24.5%
.20389
18.9%
19205
 
8.5%
06628
 
6.1%
25688
 
5.3%
)5535
 
5.1%
(5535
 
5.1%
54714
 
4.4%
33960
 
3.7%
43711
 
3.4%
Other values (5)16246
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII199426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26464
 
13.3%
.20389
 
10.2%
a13154
 
6.6%
r9456
 
4.7%
e9320
 
4.7%
19205
 
4.6%
s7567
 
3.8%
q7431
 
3.7%
t7324
 
3.7%
u6770
 
3.4%
Other values (25)82346
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3600761
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:13.203014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976289
Coefficient of variation (CV)0.56475771
Kurtosis18.212873
Mean3.3600761
Median Absolute Deviation (MAD)1
Skewness3.4851418
Sum12355
Variance3.6009954
MonotonicityNot monotonic
2025-09-27T15:46:13.571277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31496
40.7%
2942
25.6%
4660
17.9%
5210
 
5.7%
1124
 
3.4%
674
 
2.0%
941
 
1.1%
830
 
0.8%
728
 
0.8%
1228
 
0.8%
Other values (9)44
 
1.2%
ValueCountFrequency (%)
1124
 
3.4%
2942
25.6%
31496
40.7%
4660
17.9%
5210
 
5.7%
674
 
2.0%
728
 
0.8%
830
 
0.8%
941
 
1.1%
1020
 
0.5%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
192
 
0.1%
182
 
0.1%
1612
0.3%
141
 
< 0.1%
134
 
0.1%
1228
0.8%
111
 
< 0.1%
1020
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:13.908664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2025-09-27T15:46:14.248356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31077
29.3%
21047
28.5%
4820
22.3%
5294
 
8.0%
1156
 
4.2%
6117
 
3.2%
941
 
1.1%
740
 
1.1%
825
 
0.7%
1222
 
0.6%
Other values (9)38
 
1.0%
ValueCountFrequency (%)
1156
 
4.2%
21047
28.5%
31077
29.3%
4820
22.3%
5294
 
8.0%
6117
 
3.2%
740
 
1.1%
825
 
0.7%
941
 
1.1%
109
 
0.2%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
0.1%
184
 
0.1%
173
 
0.1%
168
 
0.2%
142
 
0.1%
134
 
0.1%
1222
0.6%
114
 
0.1%
109
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size209.4 KiB
3+
1172 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3187381
Min length1

Characters and Unicode

Total characters4849
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3+
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3+1172
31.9%
31074
29.2%
2884
24.0%
1365
 
9.9%
0182
 
4.9%

Length

2025-09-27T15:46:14.618792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:14.998472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
32246
61.1%
2884
 
24.0%
1365
 
9.9%
0182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
32246
46.3%
+1172
24.2%
2884
 
18.2%
1365
 
7.5%
0182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3677
75.8%
Math Symbol1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
32246
61.1%
2884
 
24.0%
1365
 
9.9%
0182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
32246
46.3%
+1172
24.2%
2884
 
18.2%
1365
 
7.5%
0182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32246
46.3%
+1172
24.2%
2884
 
18.2%
1365
 
7.5%
0182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7982504
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:15.438866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0124542
Coefficient of variation (CV)0.884412
Kurtosis4.5153928
Mean6.7982504
Median Absolute Deviation (MAD)3
Skewness1.6936988
Sum24868
Variance36.149606
MonotonicityNot monotonic
2025-09-27T15:46:15.805367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3498
13.5%
2493
13.4%
1351
 
9.5%
4316
 
8.6%
8195
 
5.3%
6183
 
5.0%
10179
 
4.9%
7176
 
4.8%
5169
 
4.6%
9161
 
4.4%
Other values (33)937
25.5%
ValueCountFrequency (%)
0129
 
3.5%
1351
9.5%
2493
13.4%
3498
13.5%
4316
8.6%
5169
 
4.6%
6183
 
5.0%
7176
 
4.8%
8195
 
5.3%
9161
 
4.4%
ValueCountFrequency (%)
511
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
432
0.1%
401
 
< 0.1%
392
0.1%
381
 
< 0.1%
352
0.1%
342
0.1%
334
0.1%

facing
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size224.3 KiB
Na
1045 
East
623 
North-East
623 
North
387 
West
249 
Other values (4)
750 

Length

Max length10
Median length5
Mean length5.4631493
Min length2

Characters and Unicode

Total characters20088
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowNa
3rd rowEast
4th rowNa
5th rowEast

Common Values

ValueCountFrequency (%)
Na1045
28.4%
East623
16.9%
North-East623
16.9%
North387
 
10.5%
West249
 
6.8%
South231
 
6.3%
North-West193
 
5.2%
South-East173
 
4.7%
South-West153
 
4.2%

Length

2025-09-27T15:46:16.143311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:16.483617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
na1045
28.4%
east623
16.9%
north-east623
16.9%
north387
 
10.5%
west249
 
6.8%
south231
 
6.3%
north-west193
 
5.2%
south-east173
 
4.7%
south-west153
 
4.2%

Most occurring characters

ValueCountFrequency (%)
t3774
18.8%
a2464
12.3%
N2248
11.2%
s2014
10.0%
o1760
8.8%
h1760
8.8%
E1419
 
7.1%
r1203
 
6.0%
-1142
 
5.7%
W595
 
3.0%
Other values (3)1709
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14127
70.3%
Uppercase Letter4819
 
24.0%
Dash Punctuation1142
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t3774
26.7%
a2464
17.4%
s2014
14.3%
o1760
12.5%
h1760
12.5%
r1203
 
8.5%
e595
 
4.2%
u557
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
N2248
46.6%
E1419
29.4%
W595
 
12.3%
S557
 
11.6%
Dash Punctuation
ValueCountFrequency (%)
-1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18946
94.3%
Common1142
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t3774
19.9%
a2464
13.0%
N2248
11.9%
s2014
10.6%
o1760
9.3%
h1760
9.3%
E1419
 
7.5%
r1203
 
6.3%
W595
 
3.1%
e595
 
3.1%
Other values (2)1114
 
5.9%
Common
ValueCountFrequency (%)
-1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t3774
18.8%
a2464
12.3%
N2248
11.2%
s2014
10.0%
o1760
8.8%
h1760
8.8%
E1419
 
7.1%
r1203
 
6.0%
-1142
 
5.7%
W595
 
3.0%
Other values (3)1709
8.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size272.2 KiB
Relatively new property
1646 
New Property
593 
Moderately New Property
563 
Undefined
306 
Old Property
303 

Length

Max length23
Median length23
Mean length18.792766
Min length9

Characters and Unicode

Total characters69101
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUndefined
2nd rowModerately New Property
3rd rowModerately New Property
4th rowNew Property
5th rowRelatively new property

Common Values

ValueCountFrequency (%)
Relatively new property1646
44.8%
New Property593
 
16.1%
Moderately New Property563
 
15.3%
Undefined306
 
8.3%
Old Property303
 
8.2%
Under Construction266
 
7.2%

Length

2025-09-27T15:46:16.844116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:17.127496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
property3105
33.5%
new2802
30.3%
relatively1646
17.8%
moderately563
 
6.1%
undefined306
 
3.3%
old303
 
3.3%
under266
 
2.9%
construction266
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e11203
16.2%
r7305
10.6%
t5846
 
8.5%
5580
 
8.1%
y5314
 
7.7%
p4751
 
6.9%
o4200
 
6.1%
l4158
 
6.0%
n3056
 
4.4%
w2802
 
4.1%
Other values (15)14886
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter57556
83.3%
Uppercase Letter5965
 
8.6%
Space Separator5580
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e11203
19.5%
r7305
12.7%
t5846
10.2%
y5314
9.2%
p4751
8.3%
o4200
 
7.3%
l4158
 
7.2%
n3056
 
5.3%
w2802
 
4.9%
i2218
 
3.9%
Other values (7)6703
11.6%
Uppercase Letter
ValueCountFrequency (%)
R1646
27.6%
P1459
24.5%
N1156
19.4%
U572
 
9.6%
M563
 
9.4%
O303
 
5.1%
C266
 
4.5%
Space Separator
ValueCountFrequency (%)
5580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63521
91.9%
Common5580
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e11203
17.6%
r7305
11.5%
t5846
9.2%
y5314
8.4%
p4751
 
7.5%
o4200
 
6.6%
l4158
 
6.5%
n3056
 
4.8%
w2802
 
4.4%
i2218
 
3.5%
Other values (14)12668
19.9%
Common
ValueCountFrequency (%)
5580
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII69101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e11203
16.2%
r7305
10.6%
t5846
 
8.5%
5580
 
8.1%
y5314
 
7.7%
p4751
 
6.9%
o4200
 
6.1%
l4158
 
6.0%
n3056
 
4.4%
w2802
 
4.1%
Other values (15)14886
21.5%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:17.649060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2025-09-27T15:46:18.123914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195037
 
1.0%
165037
 
1.0%
157825
 
0.7%
200025
 
0.7%
164022
 
0.6%
215022
 
0.6%
190019
 
0.5%
240819
 
0.5%
193018
 
0.5%
135017
 
0.5%
Other values (583)1634
44.4%
(Missing)1802
49.0%
ValueCountFrequency (%)
891
< 0.1%
1451
< 0.1%
1611
< 0.1%
2151
< 0.1%
2161
< 0.1%
3251
< 0.1%
3401
< 0.1%
3521
< 0.1%
3801
< 0.1%
4061
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
69261
< 0.1%
60001
< 0.1%
58002
0.1%
55141
< 0.1%
53502
0.1%
52002
0.1%
48901
< 0.1%
48571
< 0.1%
48482
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct644
Distinct (%)38.1%
Missing1987
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:18.533899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2025-09-27T15:46:18.888228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180041
 
1.1%
324037
 
1.0%
190034
 
0.9%
135033
 
0.9%
270033
 
0.9%
90028
 
0.8%
160026
 
0.7%
130024
 
0.7%
200024
 
0.7%
170023
 
0.6%
Other values (634)1387
37.7%
(Missing)1987
54.0%
ValueCountFrequency (%)
21
 
< 0.1%
141
 
< 0.1%
301
 
< 0.1%
331
 
< 0.1%
503
0.1%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
605
0.1%
ValueCountFrequency (%)
7371471
 
< 0.1%
135001
 
< 0.1%
112861
 
< 0.1%
95001
 
< 0.1%
90007
0.2%
87751
 
< 0.1%
82861
 
< 0.1%
8067.81
 
< 0.1%
80001
 
< 0.1%
75002
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)39.2%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:19.220847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2025-09-27T15:46:19.748182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140042
 
1.1%
180035
 
1.0%
160035
 
1.0%
120031
 
0.8%
150029
 
0.8%
165028
 
0.8%
135027
 
0.7%
130023
 
0.6%
145022
 
0.6%
100022
 
0.6%
Other values (723)1578
42.9%
(Missing)1805
49.1%
ValueCountFrequency (%)
151
 
< 0.1%
331
 
< 0.1%
481
 
< 0.1%
501
 
< 0.1%
591
 
< 0.1%
601
 
< 0.1%
661
 
< 0.1%
721
 
< 0.1%
76.443
0.1%
77.311
 
< 0.1%
ValueCountFrequency (%)
6079361
< 0.1%
5692431
< 0.1%
5143961
< 0.1%
645291
< 0.1%
644121
< 0.1%
581411
< 0.1%
549171
< 0.1%
488111
< 0.1%
459661
< 0.1%
344011
< 0.1%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2349 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Length

2025-09-27T15:46:20.196321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:20.446826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring characters

ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02349
63.9%
11328
36.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2972 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Length

2025-09-27T15:46:20.751478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:21.129393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02972
80.8%
1705
 
19.2%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3272 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Length

2025-09-27T15:46:21.388745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:21.689560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03272
89.0%
1405
 
11.0%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3021 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Length

2025-09-27T15:46:21.940058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:22.261978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03021
82.2%
1656
 
17.8%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3339 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Length

2025-09-27T15:46:22.582252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:22.842575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03339
90.8%
1338
 
9.2%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
1
2436 
0
1038 
2
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12436
66.2%
01038
28.2%
2203
 
5.5%

Length

2025-09-27T15:46:23.106359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-27T15:46:23.379291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
12436
66.2%
01038
28.2%
2203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
12436
66.2%
01038
28.2%
2203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12436
66.2%
01038
28.2%
2203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12436
66.2%
01038
28.2%
2203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12436
66.2%
01038
28.2%
2203
 
5.5%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.512918
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-27T15:46:23.682128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.059082
Coefficient of variation (CV)0.74195102
Kurtosis-0.88020421
Mean71.512918
Median Absolute Deviation (MAD)38
Skewness0.4590463
Sum262953
Variance2815.2662
MonotonicityNot monotonic
2025-09-27T15:46:24.023212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0462
 
12.6%
49348
 
9.5%
174195
 
5.3%
4460
 
1.6%
3855
 
1.5%
16555
 
1.5%
7252
 
1.4%
6047
 
1.3%
3745
 
1.2%
4245
 
1.2%
Other values (151)2313
62.9%
ValueCountFrequency (%)
0462
12.6%
56
 
0.2%
66
 
0.2%
741
 
1.1%
830
 
0.8%
99
 
0.2%
126
 
0.2%
1310
 
0.3%
1412
 
0.3%
1543
 
1.2%
ValueCountFrequency (%)
174195
5.3%
1691
 
< 0.1%
1689
 
0.2%
16721
 
0.6%
16610
 
0.3%
16555
 
1.5%
1613
 
0.1%
16028
 
0.8%
15923
 
0.6%
15834
 
0.9%

Interactions

2025-09-27T15:45:57.052204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:32.986558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:35.283310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:38.418231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:40.580307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:43.471217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:46.525557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:49.710876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:52.324931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:54.872259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:57.283501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:33.206408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:35.618924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:38.629910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:40.850429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:43.699419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:46.893178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:50.079736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:52.602581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:55.083539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:57.506431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:33.430065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:35.872760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:38.831559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:41.079835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:43.922509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:47.277475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:50.337179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:52.855009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:55.300534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:57.707211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:33.629929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:36.098045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:39.022747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:41.310727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:44.145390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:47.590469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:50.558960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:53.079472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:55.523601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:57.955173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:33.861866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:36.421703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:39.255855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:41.633132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:44.431359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:47.956617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:50.876864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:53.343389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:55.766160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:58.198015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:34.112161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:36.675896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:39.479788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:41.914325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:44.834527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:48.311051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:51.118350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:53.653834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:55.991188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:58.414343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:34.318630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:36.892002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:39.686885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:42.264039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:45.117708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:48.618734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:51.340264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:53.881867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:56.191710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:58.631038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:34.527987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:37.110388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:39.897478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:42.598443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:45.442700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:48.854944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:51.559006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:54.086492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:56.406268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:58.958682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:34.771945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:37.908610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:40.135705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:42.846223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:45.804350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:49.103328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:51.782386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:54.326280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:56.614369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:59.312093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:35.057563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:38.176341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:40.369110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:43.068058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:46.118789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:49.399416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:52.032050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:54.636758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-27T15:45:56.832964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-09-27T15:46:24.337076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2740.1110.1300.0000.0000.2030.1250.2140.2550.1080.1870.1020.0560.3790.2870.1430.1400.086
area0.0001.0000.0110.6870.6240.8350.8010.0080.1160.0430.2590.0420.0370.7440.2070.0280.0150.0390.0180.948
balcony0.2740.0111.0000.2250.1760.0000.0260.1650.0790.1780.2230.0820.1970.1360.0330.2140.4410.1460.1830.306
bathroom0.1110.6870.2251.0000.8620.4650.5990.077-0.0050.1950.1790.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom0.1300.6240.1760.8621.0000.3800.5690.052-0.1040.1660.0570.0790.2910.6810.4170.5950.3170.2230.1540.800
built_up_area0.0000.8350.0000.4650.3801.0000.9690.0000.0910.0900.2890.0000.0000.6050.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.5990.5690.9691.0000.0210.1590.0000.2390.0160.0000.6130.1360.0000.0000.0000.0030.894
facing0.2030.0080.1650.0770.0520.0000.0211.0000.0260.1950.1740.0750.1980.0450.0120.0790.2880.1360.1240.093
floorNum0.1250.1160.079-0.005-0.1040.0910.1590.0261.0000.0260.2320.0330.1020.001-0.1260.4850.0840.1120.0780.152
furnishing_type0.2140.0430.1780.1950.1660.0900.0000.1950.0261.0000.2380.0640.2130.1740.0220.0850.2660.1560.1380.132
luxury_score0.2550.2590.2230.1790.0570.2890.2390.1740.2320.2381.0000.1760.1890.2150.0540.3290.3470.2280.1830.222
others0.1080.0420.0820.0700.0790.0000.0160.0750.0330.0640.1761.0000.0330.0340.0360.0260.0000.1060.0310.084
pooja room0.1870.0370.1970.2860.2910.0000.0000.1980.1020.2130.1890.0331.0000.3340.0430.2520.2520.3050.3130.157
price0.1020.7440.1360.7200.6810.6050.6130.0450.0010.1740.2150.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sqft0.0560.2070.0330.4110.4170.1320.1360.012-0.1260.0220.0540.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.3790.0280.2140.4720.5950.0000.0000.0790.4850.0850.3290.0260.2520.5430.2011.0000.0650.2410.1281.000
servant room0.2870.0150.4410.5200.3170.0000.0000.2880.0840.2660.3470.0000.2520.3690.0440.0651.0000.1610.1850.584
store room0.1430.0390.1460.2440.2230.0000.0000.1360.1120.1560.2280.1060.3050.3030.0000.2410.1611.0000.2260.046
study room0.1400.0180.1830.1760.1540.0000.0030.1240.0780.1380.1830.0310.3130.2440.0300.1280.1850.2261.0000.121
super_built_up_area0.0860.9480.3060.8190.8000.9260.8940.0930.1520.1320.2220.0840.1570.7720.2871.0000.5840.0460.1211.000

Missing values

2025-09-27T15:45:59.791698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-27T15:46:01.195488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-27T15:46:01.702183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sectorproperty_typesocietypriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areaservant roomstudy roomotherspooja roomstore roomfurnishing_typeluxury_score
0sector 95flatthe roselia 20.456475.0695.00Built Up area: 695 (64.57 sq.m.)Carpet area: 595 sq.ft. (55.28 sq.m.)22219.0NorthUndefinedNaN695.0595.00000010
1sector 78flatumang monsoon breeze1.206493.01848.00Super Built up area 2250(209.03 sq.m.)Carpet area: 1848 sq.ft. (171.68 sq.m.)3328.0NaModerately New Property2250.0NaN1848.00001028
2manesarflathsiidc sidco aravali0.913516.02588.00Super Built up area 2588(240.43 sq.m.)Built Up area: 1900 sq.ft. (176.52 sq.m.)333+8.0EastModerately New Property2588.01900.0NaN10000123
3sector 108flatsobha city3.3513266.02525.00Super Built up area 2072.9(192.58 sq.m.)34219.0NaNew Property2072.9NaNNaN000001102
4sector 108flatsobha city1.9013758.01381.00Super Built up area 1381(128.3 sq.m.)2228.0EastRelatively new property1381.0NaNNaN00000185
5sector 65flatm3m heights2.1515003.01433.00Built Up area: 1433 (133.13 sq.m.)223+28.0EastUndefinedNaN1433.0NaN00000148
6sector 86flatdlf new town heights1.405942.02356.00Super Built up area 2356(218.88 sq.m.)443+20.0South-EastRelatively new property2356.0NaNNaN10010165
7sector 3houseindependent0.377916.0467.41Built Up area: 480 (44.59 sq.m.)1111.0NaRelatively new propertyNaN480.0NaN0000010
8sector 79flatgodrej1.298206.01572.00Super Built up area 1572(146.04 sq.m.)2234.0NaNew Property1572.0NaNNaN0000010
9sector 43houseindependent7.8529074.02700.01Plot area 300(250.84 sq.m.)993+3.0WestRelatively new propertyNaN2700.0NaN11110249
sectorproperty_typesocietypriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areaservant roomstudy roomotherspooja roomstore roomfurnishing_typeluxury_score
3793sector 85flatss the leaf2.205641.03900.00Super Built up area 3950(366.97 sq.m.)Built Up area: 3920 sq.ft. (364.18 sq.m.)Carpet area: 3900 sq.ft. (362.32 sq.m.)46310.0North-WestRelatively new property3950.03920.03900.0100001174
3794sector 4houseindependent0.554074.01350.02Plot area 1350(125.42 sq.m.)3322.0SouthRelatively new propertyNaN1350.0NaN0010017
3795sector 33flatashiana anmol1.108627.01275.00Built Up area: 1275 (118.45 sq.m.)Carpet area: 795 sq.ft. (73.86 sq.m.)2224.0North-EastUndefinedNaN1275.0795.00000010
3796sector 102flatemaar imperial gardens2.059876.02076.00Carpet area: 2025 (188.13 sq.m.)343+14.0SouthRelatively new propertyNaNNaN2025.0100001116
3797sector 109flatats kocoon2.1510262.02095.00Super Built up area 2095(194.63 sq.m.)3439.0NorthRelatively new property2095.0NaNNaN10000197
3798sector 102flatemaar gurgaon greens1.458787.01650.00Super Built up area 1650(153.29 sq.m.)3327.0South-WestRelatively new property1650.0NaNNaN100001169
3799sector 76flatsuncity avenue 760.7510204.0735.00Super Built up area 735(68.28 sq.m.)22211.0NaUnder Construction735.0NaNNaN00000157
3800sector 65flatm3m golfestate7.0018181.03850.00Carpet area: 3850 (357.68 sq.m.)33212.0NorthModerately New PropertyNaNNaN3850.000001160
3801sector 86flatdlf the skycourt1.608294.01929.00Super Built up area 1929(179.21 sq.m.)33114.0North-EastRelatively new property1929.0NaNNaN00010172
3802sector 2flatbestech park view residency1.517869.01919.00Super Built up area 1920(178.37 sq.m.)343+7.0South-WestModerately New Property1920.0NaNNaN10000084